When group labels are available at training time, the problem is well-posed and the algorithmic bias that the studied models have learned can be erased. For instance, in [1]}, Zhang et al. proposed to employ an adversarial network to modify the latent space of the classifier to optimize a given fairness metric. In a similar way, Kim et al. [2]} used an adversary to minimize the mutual information between the latent space and the sensitive variable. Grari et al. [3]} also adversarially optimized the Hirschfeld‐Gebelein‐Rényi (HGR) maximal correlation coefficient.
Penalty terms were also used in [4]}, [5]} to ensure a good balance between model accuracy and fairness properties with respect to a sensitive variable.
In a somewhat different path, other techniques piggyback on the fact that disentangled representations can be more fair than standard ones, as the important information for the prediction is separated from the sensitive variable [6]}. In particular, this has been applied in [7]}, [8]} to split the latent space into two groups through the use of specific losses, with the task information on one side and the sensitive variable on the other.
